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A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels

Author

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  • Manago, Kimberly F.
  • Hogue, Terri S.
  • Porter, Aaron
  • Hering, Amanda S.

Abstract

Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.

Suggested Citation

  • Manago, Kimberly F. & Hogue, Terri S. & Porter, Aaron & Hering, Amanda S., 2019. "A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 44-51.
    • Handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:44-51
    DOI: 10.1016/j.spl.2018.07.023
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    References listed on IDEAS

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    1. Philip K. Hopke & Chuanhai Liu & Donald B. Rubin, 2001. "Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic," Biometrics, The International Biometric Society, vol. 57(1), pages 22-33, March.
    2. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
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    Cited by:

    1. Marcus L. Nascimento & Kelly C. M. Gonçalves & Mario Jorge Mendonça, 2023. "Spatio-Temporal Instrumental Variables Regression with Missing Data: A Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 29-47, June.

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